10 citations · 29 across the 14 of their papers we have counts for
16 papers
BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction
Ruochen Li, Stamos Katsigiannis, Tae-Kyun Kim +1
Trajectory prediction allows better decision-making in applications of autonomous vehicles or surveillance by predicting the short-term future movement of traffic agents. It is cla…
Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
Ruochen Li, Tanqiu Qiao, Stamos Katsigiannis +2
Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedest…
One-Index Vector Quantization Based Adversarial Attack on Image Classification
Haiju Fan, Xiaona Qin, Shuang Chen +2
To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses ot…
3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models
Ziyi Chang, George Alex Koulieris, Hubert P. H. Shum
Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual reality (VR) museums, can be labour-intensive and sometimes infeasible. Deep learning based 3D reconstr…
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Edmund J. C. Findlay, Haozheng Zhang, Ziyi Chang +1
Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years t…
CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy
Haozheng Zhang, Edmond S. L. Ho, Hubert P. H. Shum
Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system fo…